class_docstr: A helper class to be used with the __getitem__ method. This can be used for
  getting/setting the values for an attribute of a class at one particular
  index.  This is useful when the attributes of a class are batched tensors
  and one element in the batch needs to be modified.
docstr: Args:
      class_object: this should be an instance of a class which has
          attributes which are tensors representing a batch of
          values.
      index: int/slice, an index indicating the position in the batch.
          In __setattr__ and __getattr__ only the value of class
          attributes at this index will be accessed.
docstr_tok: ["Args",":","class_object",":","this","should","be","an","instance","of","a","class","which","has","attributes","which","are","tensors","representing","a","batch","of","values",".","index",":","int\/slice",",","an","index","indicating","the","position","in","the","batch",".","In","__setattr__","and","__getattr__","only","the","value","of","class","attributes","at","this","index","will","be","accessed","."]
code: def __init__(self, class_object, index: Union[int, slice]) -> None:
      """
      Args:
          class_object: this should be an instance of a class which has
              attributes which are tensors representing a batch of
              values.
          index: int/slice, an index indicating the position in the batch.
              In __setattr__ and __getattr__ only the value of class
              attributes at this index will be accessed.
      """
      self.__dict__["class_object"] = class_object
      self.__dict__["index"] = index
  
imports: import copy
  import inspect
  import warnings
  from typing import Any, List, Optional, Tuple, TypeVar, Union
  import numpy
  import torch
  import torch.nn
  from ..common.datatypes import Device, make_device
label_desc: Use image node_id 1 to create a new TensorAccessor object from inherited base classes: nn with example: obj = TensorAccessor(class_object, index)